Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = '/input'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:02, 4.23MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:11<00:00, 5.30KFile/s] 
Downloading celeba: 1.44GB [00:35, 40.1MB/s]                              
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f16fd01ca58>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f17052274e0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, shape=[None, image_width, image_height, image_channels], name='input_real') 
    input_z = tf.placeholder(tf.float32, shape=[None, z_dim], name='input_z')
    learning_rate = tf.placeholder(tf.float32, shape=None, name='learning_rate')
    return input_real, input_z, learning_rate



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha=0.2
    x = images
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(x, 64, 5, strides=2, padding="same")
        bn1 = tf.layers.batch_normalization(x1, training=True)
        relu1 = tf.maximum(alpha * bn1, bn1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding="same")
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding="same")
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        flat = tf.reshape(relu3, (-1, 4 * 4 * 256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha=0.2
    reuse = not is_train
    with tf.variable_scope('generator', reuse=reuse):
        x1 = tf.layers.dense(z, 4 * 4 * 512)     
        x1 = tf.reshape(x1, (-1, 4, 4, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=1, padding="same")
        x2 = tf.layers.batch_normalization(x2,training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding="same")
        x3 = tf.layers.batch_normalization(x3,training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding="same")
        out = tf.tanh(logits)
    return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    fake = generator(input_z, out_channel_dim, is_train=True)
    d_logits_fake = discriminator(fake, reuse=True)
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,labels=tf.ones_like(d_logits_real) * (0.9)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,labels=tf.zeros_like(d_logits_fake)))
    d_loss = d_loss_real + d_loss_fake
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    all_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    g_update_ops = [var for var in all_update_ops if var.name.startswith('generator')]
    d_update_ops = [var for var in all_update_ops if var.name.startswith('discriminator')]
    
    with tf.control_dependencies(d_update_ops):
        d_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(d_loss, var_list=d_vars)
    with tf.control_dependencies(g_update_ops):
        g_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(g_loss, var_list=g_vars)
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    inputs_real, inputs_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(inputs_real, inputs_z, data_shape[-1])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    step = 0 
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                step += 1
                batch_images *= 2
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                #print('batch_z shape=',batch_z.shape)
                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={inputs_real: batch_images, inputs_z: batch_z, lr:learning_rate})
                _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z, lr:learning_rate})
                
                if step % 100 == 0:
                    train_loss_d = d_loss.eval({inputs_z:batch_z, inputs_real: batch_images})
                    train_loss_g = g_loss.eval({inputs_z:batch_z})
                    print("Epoch {}/{} Step {}...".format(epoch_i+1, epoch_count, step),
                      "Discriminator Loss: {:.4f}...".format(train_loss_d),
                      "Generator Loss: {:.4f}".format(train_loss_g))    

                if step % 200 == 0:
                    show_generator_output(sess, 25, inputs_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 10

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/10 Step 100... Discriminator Loss: 1.2081... Generator Loss: 1.3460
Epoch 1/10 Step 200... Discriminator Loss: 1.1805... Generator Loss: 1.0290
Epoch 1/10 Step 300... Discriminator Loss: 1.3500... Generator Loss: 0.4858
Epoch 1/10 Step 400... Discriminator Loss: 1.0967... Generator Loss: 0.7441
Epoch 1/10 Step 500... Discriminator Loss: 1.1290... Generator Loss: 0.6785
Epoch 1/10 Step 600... Discriminator Loss: 1.3110... Generator Loss: 1.1425
Epoch 1/10 Step 700... Discriminator Loss: 1.1678... Generator Loss: 0.6432
Epoch 1/10 Step 800... Discriminator Loss: 1.1521... Generator Loss: 0.7883
Epoch 1/10 Step 900... Discriminator Loss: 1.4626... Generator Loss: 0.4147
Epoch 2/10 Step 1000... Discriminator Loss: 1.2927... Generator Loss: 1.0636
Epoch 2/10 Step 1100... Discriminator Loss: 1.4098... Generator Loss: 0.4361
Epoch 2/10 Step 1200... Discriminator Loss: 1.4671... Generator Loss: 0.4150
Epoch 2/10 Step 1300... Discriminator Loss: 1.5276... Generator Loss: 0.3887
Epoch 2/10 Step 1400... Discriminator Loss: 1.3051... Generator Loss: 0.5026
Epoch 2/10 Step 1500... Discriminator Loss: 1.2988... Generator Loss: 1.1336
Epoch 2/10 Step 1600... Discriminator Loss: 1.2410... Generator Loss: 0.7966
Epoch 2/10 Step 1700... Discriminator Loss: 1.1725... Generator Loss: 0.6271
Epoch 2/10 Step 1800... Discriminator Loss: 1.0961... Generator Loss: 0.7787
Epoch 3/10 Step 1900... Discriminator Loss: 1.2016... Generator Loss: 1.2016
Epoch 3/10 Step 2000... Discriminator Loss: 1.1497... Generator Loss: 0.6671
Epoch 3/10 Step 2100... Discriminator Loss: 1.1161... Generator Loss: 0.7018
Epoch 3/10 Step 2200... Discriminator Loss: 1.1800... Generator Loss: 1.4004
Epoch 3/10 Step 2300... Discriminator Loss: 0.9522... Generator Loss: 1.2531
Epoch 3/10 Step 2400... Discriminator Loss: 0.9568... Generator Loss: 1.0527
Epoch 3/10 Step 2500... Discriminator Loss: 1.6930... Generator Loss: 0.3526
Epoch 3/10 Step 2600... Discriminator Loss: 1.2292... Generator Loss: 1.2717
Epoch 3/10 Step 2700... Discriminator Loss: 2.0299... Generator Loss: 0.2927
Epoch 3/10 Step 2800... Discriminator Loss: 1.0036... Generator Loss: 1.0434
Epoch 4/10 Step 2900... Discriminator Loss: 1.1071... Generator Loss: 0.6962
Epoch 4/10 Step 3000... Discriminator Loss: 0.9343... Generator Loss: 1.0036
Epoch 4/10 Step 3100... Discriminator Loss: 1.4666... Generator Loss: 0.4439
Epoch 4/10 Step 3200... Discriminator Loss: 0.9468... Generator Loss: 1.0126
Epoch 4/10 Step 3300... Discriminator Loss: 0.9165... Generator Loss: 1.0521
Epoch 4/10 Step 3400... Discriminator Loss: 1.0102... Generator Loss: 0.8430
Epoch 4/10 Step 3500... Discriminator Loss: 0.9474... Generator Loss: 0.9797
Epoch 4/10 Step 3600... Discriminator Loss: 1.0327... Generator Loss: 0.7988
Epoch 4/10 Step 3700... Discriminator Loss: 2.0810... Generator Loss: 0.2829
Epoch 5/10 Step 3800... Discriminator Loss: 1.0289... Generator Loss: 0.8191
Epoch 5/10 Step 3900... Discriminator Loss: 1.0279... Generator Loss: 1.3765
Epoch 5/10 Step 4000... Discriminator Loss: 0.9018... Generator Loss: 1.2091
Epoch 5/10 Step 4100... Discriminator Loss: 0.9251... Generator Loss: 1.1714
Epoch 5/10 Step 4200... Discriminator Loss: 1.0526... Generator Loss: 0.7617
Epoch 5/10 Step 4300... Discriminator Loss: 1.1384... Generator Loss: 0.6694
Epoch 5/10 Step 4400... Discriminator Loss: 1.0671... Generator Loss: 0.7910
Epoch 5/10 Step 4500... Discriminator Loss: 1.3081... Generator Loss: 0.5558
Epoch 5/10 Step 4600... Discriminator Loss: 1.1542... Generator Loss: 0.6625
Epoch 6/10 Step 4700... Discriminator Loss: 0.8744... Generator Loss: 1.2796
Epoch 6/10 Step 4800... Discriminator Loss: 1.0519... Generator Loss: 1.2609
Epoch 6/10 Step 4900... Discriminator Loss: 0.9296... Generator Loss: 0.9967
Epoch 6/10 Step 5000... Discriminator Loss: 1.0220... Generator Loss: 0.8506
Epoch 6/10 Step 5100... Discriminator Loss: 0.9834... Generator Loss: 0.9035
Epoch 6/10 Step 5200... Discriminator Loss: 0.9002... Generator Loss: 1.2157
Epoch 6/10 Step 5300... Discriminator Loss: 0.8586... Generator Loss: 1.2117
Epoch 6/10 Step 5400... Discriminator Loss: 0.9763... Generator Loss: 0.9407
Epoch 6/10 Step 5500... Discriminator Loss: 0.9370... Generator Loss: 1.1868
Epoch 6/10 Step 5600... Discriminator Loss: 1.2528... Generator Loss: 0.5854
Epoch 7/10 Step 5700... Discriminator Loss: 1.0569... Generator Loss: 0.8221
Epoch 7/10 Step 5800... Discriminator Loss: 0.9332... Generator Loss: 1.3633
Epoch 7/10 Step 5900... Discriminator Loss: 0.9005... Generator Loss: 1.0914
Epoch 7/10 Step 6000... Discriminator Loss: 1.3284... Generator Loss: 0.5356
Epoch 7/10 Step 6100... Discriminator Loss: 1.0771... Generator Loss: 0.7392
Epoch 7/10 Step 6200... Discriminator Loss: 0.9291... Generator Loss: 1.0673
Epoch 7/10 Step 6300... Discriminator Loss: 1.8814... Generator Loss: 0.3466
Epoch 7/10 Step 6400... Discriminator Loss: 1.8681... Generator Loss: 0.3210
Epoch 7/10 Step 6500... Discriminator Loss: 1.0112... Generator Loss: 1.3491
Epoch 8/10 Step 6600... Discriminator Loss: 0.9687... Generator Loss: 0.9505
Epoch 8/10 Step 6700... Discriminator Loss: 1.1540... Generator Loss: 0.6540
Epoch 8/10 Step 6800... Discriminator Loss: 1.3083... Generator Loss: 0.5235
Epoch 8/10 Step 6900... Discriminator Loss: 0.9606... Generator Loss: 1.1030
Epoch 8/10 Step 7000... Discriminator Loss: 0.8581... Generator Loss: 1.1867
Epoch 8/10 Step 7100... Discriminator Loss: 0.8108... Generator Loss: 1.4015
Epoch 8/10 Step 7200... Discriminator Loss: 0.9578... Generator Loss: 0.9928
Epoch 8/10 Step 7300... Discriminator Loss: 0.9919... Generator Loss: 0.8944
Epoch 8/10 Step 7400... Discriminator Loss: 0.9721... Generator Loss: 0.9047
Epoch 9/10 Step 7500... Discriminator Loss: 1.1537... Generator Loss: 0.6628
Epoch 9/10 Step 7600... Discriminator Loss: 0.9416... Generator Loss: 0.9979
Epoch 9/10 Step 7700... Discriminator Loss: 0.8952... Generator Loss: 1.1046
Epoch 9/10 Step 7800... Discriminator Loss: 1.1987... Generator Loss: 0.6904
Epoch 9/10 Step 7900... Discriminator Loss: 0.8463... Generator Loss: 1.2610
Epoch 9/10 Step 8000... Discriminator Loss: 1.2481... Generator Loss: 0.6140
Epoch 9/10 Step 8100... Discriminator Loss: 0.9754... Generator Loss: 0.8739
Epoch 9/10 Step 8200... Discriminator Loss: 0.8228... Generator Loss: 1.2809
Epoch 9/10 Step 8300... Discriminator Loss: 1.0061... Generator Loss: 1.3690
Epoch 9/10 Step 8400... Discriminator Loss: 1.1136... Generator Loss: 0.7196
Epoch 10/10 Step 8500... Discriminator Loss: 1.0408... Generator Loss: 0.8992
Epoch 10/10 Step 8600... Discriminator Loss: 0.7502... Generator Loss: 1.7393
Epoch 10/10 Step 8700... Discriminator Loss: 1.0761... Generator Loss: 0.7866
Epoch 10/10 Step 8800... Discriminator Loss: 0.9164... Generator Loss: 1.0683
Epoch 10/10 Step 8900... Discriminator Loss: 1.3260... Generator Loss: 0.5837
Epoch 10/10 Step 9000... Discriminator Loss: 1.3439... Generator Loss: 0.5646
Epoch 10/10 Step 9100... Discriminator Loss: 0.8697... Generator Loss: 1.1303
Epoch 10/10 Step 9200... Discriminator Loss: 0.8800... Generator Loss: 1.2138
Epoch 10/10 Step 9300... Discriminator Loss: 0.9732... Generator Loss: 0.9177

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [14]:
batch_size = 128
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 7

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/7 Step 100... Discriminator Loss: 1.2458... Generator Loss: 0.7508
Epoch 1/7 Step 200... Discriminator Loss: 1.3337... Generator Loss: 1.0322
Epoch 1/7 Step 300... Discriminator Loss: 1.4631... Generator Loss: 0.4591
Epoch 1/7 Step 400... Discriminator Loss: 1.5425... Generator Loss: 0.5440
Epoch 1/7 Step 500... Discriminator Loss: 1.5170... Generator Loss: 0.4467
Epoch 1/7 Step 600... Discriminator Loss: 1.5576... Generator Loss: 1.0948
Epoch 1/7 Step 700... Discriminator Loss: 1.2884... Generator Loss: 0.6332
Epoch 1/7 Step 800... Discriminator Loss: 1.3805... Generator Loss: 0.5914
Epoch 1/7 Step 900... Discriminator Loss: 1.5112... Generator Loss: 0.7813
Epoch 1/7 Step 1000... Discriminator Loss: 1.5695... Generator Loss: 0.6550
Epoch 1/7 Step 1100... Discriminator Loss: 1.3716... Generator Loss: 0.6505
Epoch 1/7 Step 1200... Discriminator Loss: 1.3585... Generator Loss: 1.1456
Epoch 1/7 Step 1300... Discriminator Loss: 1.2764... Generator Loss: 0.6592
Epoch 1/7 Step 1400... Discriminator Loss: 1.3261... Generator Loss: 0.6115
Epoch 1/7 Step 1500... Discriminator Loss: 1.2999... Generator Loss: 0.8114
Epoch 2/7 Step 1600... Discriminator Loss: 1.3717... Generator Loss: 0.5995
Epoch 2/7 Step 1700... Discriminator Loss: 1.4400... Generator Loss: 0.6557
Epoch 2/7 Step 1800... Discriminator Loss: 1.3018... Generator Loss: 0.8763
Epoch 2/7 Step 1900... Discriminator Loss: 1.5357... Generator Loss: 0.9115
Epoch 2/7 Step 2000... Discriminator Loss: 1.4102... Generator Loss: 0.4714
Epoch 2/7 Step 2100... Discriminator Loss: 1.3329... Generator Loss: 0.6802
Epoch 2/7 Step 2200... Discriminator Loss: 1.5414... Generator Loss: 0.4002
Epoch 2/7 Step 2300... Discriminator Loss: 1.3244... Generator Loss: 0.6396
Epoch 2/7 Step 2400... Discriminator Loss: 1.3136... Generator Loss: 0.8688
Epoch 2/7 Step 2500... Discriminator Loss: 1.3548... Generator Loss: 0.5694
Epoch 2/7 Step 2600... Discriminator Loss: 1.4690... Generator Loss: 0.4513
Epoch 2/7 Step 2700... Discriminator Loss: 1.4507... Generator Loss: 0.4740
Epoch 2/7 Step 2800... Discriminator Loss: 1.3960... Generator Loss: 0.5270
Epoch 2/7 Step 2900... Discriminator Loss: 1.4301... Generator Loss: 0.9077
Epoch 2/7 Step 3000... Discriminator Loss: 1.4501... Generator Loss: 0.4860
Epoch 2/7 Step 3100... Discriminator Loss: 1.4394... Generator Loss: 0.4762
Epoch 3/7 Step 3200... Discriminator Loss: 1.4203... Generator Loss: 0.7357
Epoch 3/7 Step 3300... Discriminator Loss: 1.4495... Generator Loss: 0.5336
Epoch 3/7 Step 3400... Discriminator Loss: 1.3336... Generator Loss: 0.7068
Epoch 3/7 Step 3500... Discriminator Loss: 1.3538... Generator Loss: 0.6201
Epoch 3/7 Step 3600... Discriminator Loss: 1.4588... Generator Loss: 0.8351
Epoch 3/7 Step 3700... Discriminator Loss: 1.6463... Generator Loss: 0.9915
Epoch 3/7 Step 3800... Discriminator Loss: 1.6347... Generator Loss: 0.3524
Epoch 3/7 Step 3900... Discriminator Loss: 1.5036... Generator Loss: 0.7185
Epoch 3/7 Step 4000... Discriminator Loss: 1.4349... Generator Loss: 0.5657
Epoch 3/7 Step 4100... Discriminator Loss: 1.4486... Generator Loss: 0.8613
Epoch 3/7 Step 4200... Discriminator Loss: 1.7651... Generator Loss: 0.3082
Epoch 3/7 Step 4300... Discriminator Loss: 1.4079... Generator Loss: 0.5985
Epoch 3/7 Step 4400... Discriminator Loss: 1.4586... Generator Loss: 0.5559
Epoch 3/7 Step 4500... Discriminator Loss: 1.4059... Generator Loss: 0.6241
Epoch 3/7 Step 4600... Discriminator Loss: 1.2599... Generator Loss: 0.6458
Epoch 3/7 Step 4700... Discriminator Loss: 1.5207... Generator Loss: 0.6960
Epoch 4/7 Step 4800... Discriminator Loss: 1.8784... Generator Loss: 0.2782
Epoch 4/7 Step 4900... Discriminator Loss: 1.4016... Generator Loss: 0.7135
Epoch 4/7 Step 5000... Discriminator Loss: 1.4222... Generator Loss: 0.5543
Epoch 4/7 Step 5100... Discriminator Loss: 1.1849... Generator Loss: 0.9353
Epoch 4/7 Step 5200... Discriminator Loss: 1.3622... Generator Loss: 0.6004
Epoch 4/7 Step 5300... Discriminator Loss: 1.3742... Generator Loss: 0.7448
Epoch 4/7 Step 5400... Discriminator Loss: 1.6276... Generator Loss: 0.3529
Epoch 4/7 Step 5500... Discriminator Loss: 1.3428... Generator Loss: 0.7166
Epoch 4/7 Step 5600... Discriminator Loss: 1.7283... Generator Loss: 0.3220
Epoch 4/7 Step 5700... Discriminator Loss: 1.4562... Generator Loss: 0.4483
Epoch 4/7 Step 5800... Discriminator Loss: 1.2801... Generator Loss: 0.7859
Epoch 4/7 Step 5900... Discriminator Loss: 1.3397... Generator Loss: 0.9175
Epoch 4/7 Step 6000... Discriminator Loss: 1.4722... Generator Loss: 0.4895
Epoch 4/7 Step 6100... Discriminator Loss: 1.4719... Generator Loss: 0.4534
Epoch 4/7 Step 6200... Discriminator Loss: 1.1370... Generator Loss: 1.3406
Epoch 4/7 Step 6300... Discriminator Loss: 1.2919... Generator Loss: 0.8197
Epoch 5/7 Step 6400... Discriminator Loss: 1.5712... Generator Loss: 1.1289
Epoch 5/7 Step 6500... Discriminator Loss: 1.5509... Generator Loss: 0.3834
Epoch 5/7 Step 6600... Discriminator Loss: 1.5554... Generator Loss: 0.3827
Epoch 5/7 Step 6700... Discriminator Loss: 1.3187... Generator Loss: 0.5571
Epoch 5/7 Step 6800... Discriminator Loss: 1.4007... Generator Loss: 0.8166
Epoch 5/7 Step 6900... Discriminator Loss: 1.2928... Generator Loss: 0.5728
Epoch 5/7 Step 7000... Discriminator Loss: 1.3418... Generator Loss: 0.8743
Epoch 5/7 Step 7100... Discriminator Loss: 1.4530... Generator Loss: 0.4371
Epoch 5/7 Step 7200... Discriminator Loss: 1.3987... Generator Loss: 1.0523
Epoch 5/7 Step 7300... Discriminator Loss: 1.1862... Generator Loss: 0.8879
Epoch 5/7 Step 7400... Discriminator Loss: 1.1066... Generator Loss: 0.8527
Epoch 5/7 Step 7500... Discriminator Loss: 1.3965... Generator Loss: 0.4945
Epoch 5/7 Step 7600... Discriminator Loss: 1.5833... Generator Loss: 0.3731
Epoch 5/7 Step 7700... Discriminator Loss: 1.2837... Generator Loss: 0.6767
Epoch 5/7 Step 7800... Discriminator Loss: 1.0327... Generator Loss: 0.9510
Epoch 5/7 Step 7900... Discriminator Loss: 1.3333... Generator Loss: 0.5222
Epoch 6/7 Step 8000... Discriminator Loss: 1.4171... Generator Loss: 0.4592
Epoch 6/7 Step 8100... Discriminator Loss: 1.2535... Generator Loss: 0.8550
Epoch 6/7 Step 8200... Discriminator Loss: 1.3284... Generator Loss: 0.7474
Epoch 6/7 Step 8300... Discriminator Loss: 1.2397... Generator Loss: 0.6827
Epoch 6/7 Step 8400... Discriminator Loss: 1.4080... Generator Loss: 0.4656
Epoch 6/7 Step 8500... Discriminator Loss: 1.1202... Generator Loss: 0.7117
Epoch 6/7 Step 8600... Discriminator Loss: 1.3680... Generator Loss: 0.4859
Epoch 6/7 Step 8700... Discriminator Loss: 1.0318... Generator Loss: 0.9143
Epoch 6/7 Step 8800... Discriminator Loss: 1.1983... Generator Loss: 0.6728
Epoch 6/7 Step 8900... Discriminator Loss: 1.4180... Generator Loss: 0.4675
Epoch 6/7 Step 9000... Discriminator Loss: 1.3385... Generator Loss: 0.5244
Epoch 6/7 Step 9100... Discriminator Loss: 1.3475... Generator Loss: 0.5194
Epoch 6/7 Step 9200... Discriminator Loss: 1.7494... Generator Loss: 0.3339
Epoch 6/7 Step 9300... Discriminator Loss: 1.1063... Generator Loss: 1.0221
Epoch 6/7 Step 9400... Discriminator Loss: 1.2055... Generator Loss: 1.0387
Epoch 7/7 Step 9500... Discriminator Loss: 1.2009... Generator Loss: 1.3493
Epoch 7/7 Step 9600... Discriminator Loss: 2.0199... Generator Loss: 0.2841
Epoch 7/7 Step 9700... Discriminator Loss: 1.0212... Generator Loss: 0.9005
Epoch 7/7 Step 9800... Discriminator Loss: 1.2491... Generator Loss: 0.6407
Epoch 7/7 Step 9900... Discriminator Loss: 1.7515... Generator Loss: 0.3664
Epoch 7/7 Step 10000... Discriminator Loss: 1.1447... Generator Loss: 1.1191
Epoch 7/7 Step 10100... Discriminator Loss: 1.1571... Generator Loss: 1.0785
Epoch 7/7 Step 10200... Discriminator Loss: 1.3725... Generator Loss: 1.5141
Epoch 7/7 Step 10300... Discriminator Loss: 1.4034... Generator Loss: 0.4896
Epoch 7/7 Step 10400... Discriminator Loss: 1.1196... Generator Loss: 0.7001
Epoch 7/7 Step 10500... Discriminator Loss: 1.0104... Generator Loss: 0.9257
Epoch 7/7 Step 10600... Discriminator Loss: 1.1213... Generator Loss: 0.8596
Epoch 7/7 Step 10700... Discriminator Loss: 1.1833... Generator Loss: 0.8902
Epoch 7/7 Step 10800... Discriminator Loss: 1.2279... Generator Loss: 1.4420
Epoch 7/7 Step 10900... Discriminator Loss: 1.4909... Generator Loss: 0.4644
Epoch 7/7 Step 11000... Discriminator Loss: 1.0804... Generator Loss: 0.8516

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.